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Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes
Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222078/ https://www.ncbi.nlm.nih.gov/pubmed/35741192 http://dx.doi.org/10.3390/diagnostics12061382 |
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author | Sánchez-Morales, Adrián Morales-Sánchez, Juan Kovalyk, Oleksandr Verdú-Monedero, Rafael Sancho-Gómez, José-Luis |
author_facet | Sánchez-Morales, Adrián Morales-Sánchez, Juan Kovalyk, Oleksandr Verdú-Monedero, Rafael Sancho-Gómez, José-Luis |
author_sort | Sánchez-Morales, Adrián |
collection | PubMed |
description | Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. In particular, deep learning tools have been successfully applied obtaining, in some cases, results superior to those obtained by humans. In this article, we present a soft novel ensemble model based on the K-NN algorithm, that combines the probability of class membership obtained by several deep learning models. In this research, three models of different nature (CNN, CapsNets and Convolutional Autoencoders) have been selected searching for diversity. The latent space of these models are combined using the local information provided by the true sample labels and the K-NN algorithm is applied to determine the final decision. The results obtained on two different datasets of retinal images show that the proposed ensemble model improves the diagnosis capabilities for both the individual models and the state-of-the-art results. |
format | Online Article Text |
id | pubmed-9222078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92220782022-06-24 Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes Sánchez-Morales, Adrián Morales-Sánchez, Juan Kovalyk, Oleksandr Verdú-Monedero, Rafael Sancho-Gómez, José-Luis Diagnostics (Basel) Article Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. In particular, deep learning tools have been successfully applied obtaining, in some cases, results superior to those obtained by humans. In this article, we present a soft novel ensemble model based on the K-NN algorithm, that combines the probability of class membership obtained by several deep learning models. In this research, three models of different nature (CNN, CapsNets and Convolutional Autoencoders) have been selected searching for diversity. The latent space of these models are combined using the local information provided by the true sample labels and the K-NN algorithm is applied to determine the final decision. The results obtained on two different datasets of retinal images show that the proposed ensemble model improves the diagnosis capabilities for both the individual models and the state-of-the-art results. MDPI 2022-06-02 /pmc/articles/PMC9222078/ /pubmed/35741192 http://dx.doi.org/10.3390/diagnostics12061382 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sánchez-Morales, Adrián Morales-Sánchez, Juan Kovalyk, Oleksandr Verdú-Monedero, Rafael Sancho-Gómez, José-Luis Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title | Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title_full | Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title_fullStr | Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title_full_unstemmed | Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title_short | Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes |
title_sort | improving glaucoma diagnosis assembling deep networks and voting schemes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222078/ https://www.ncbi.nlm.nih.gov/pubmed/35741192 http://dx.doi.org/10.3390/diagnostics12061382 |
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